"""
Utility functions for uplift trees.
"""

import numpy as np
import pandas as pd


def cat_group(dfx, kpix, n_group=10):
    """
    Category Reduction for Categorical Variables

    Args
    ----

    dfx : dataframe
        The inputs data dataframe.

    kpix : string
        The column of the feature.

    n_group : int, optional (default = 10)
        The number of top category values to be remained, other category values will be put into "Other".

    Returns
    -------
    The transformed categorical feature value list.
    """
    if dfx[kpix].nunique() > n_group:
        # get the top categories
        top = dfx[kpix].isin(dfx[kpix].value_counts().index[:n_group])
        dfx.loc[~top, kpix] = "Other"
        return dfx[kpix].values
    else:
        return dfx[kpix].values


def cat_transform(dfx, kpix, kpi1):
    """
    Encoding string features.

    Args
    ----

    dfx : dataframe
        The inputs data dataframe.

    kpix : string
        The column of the feature.

    kpi1 : list
        The list of feature names.

    Returns
    -------
    dfx : DataFrame
        The updated dataframe containing the encoded data.

    kpi1 : list
        The updated feature names containing the new dummy feature names.
    """
    df_dummy = pd.get_dummies(dfx[kpix].values)
    new_col_names = ["%s_%s" % (kpix, x) for x in df_dummy.columns]
    df_dummy.columns = new_col_names
    dfx = pd.concat([dfx, df_dummy], axis=1)
    for new_col in new_col_names:
        if new_col not in kpi1:
            kpi1.append(new_col)
    if kpix in kpi1:
        kpi1.remove(kpix)
    return dfx, kpi1


def cv_fold_index(n, i, k, random_seed=2018):
    """
    Encoding string features.

    Args
    ----

    dfx : dataframe
        The inputs data dataframe.

    kpix : string
        The column of the feature.

    kpi1 : list
        The list of feature names.

    Returns
    -------
    dfx : DataFrame
        The updated dataframe containing the encoded data.

    kpi1 : list
        The updated feature names containing the new dummy feature names.
    """
    np.random.seed(random_seed)
    rlist = np.random.choice(a=range(k), size=n, replace=True)
    fold_i_index = np.where(rlist == i)[0]
    return fold_i_index


# Categorize continuous variable
def cat_continuous(x, granularity="Medium"):
    """
    Categorize (bin) continuous variable based on percentile.

    Args
    ----

    x : list
        Feature values.

    granularity : string, optional, (default = 'Medium')
        Control the granularity of the bins, optional values are: 'High', 'Medium', 'Low'.

    Returns
    -------
    res : list
        List of percentile bins for the feature value.
    """
    if granularity == "High":
        lspercentile = [
            np.percentile(x, 5),
            np.percentile(x, 10),
            np.percentile(x, 15),
            np.percentile(x, 20),
            np.percentile(x, 25),
            np.percentile(x, 30),
            np.percentile(x, 35),
            np.percentile(x, 40),
            np.percentile(x, 45),
            np.percentile(x, 50),
            np.percentile(x, 55),
            np.percentile(x, 60),
            np.percentile(x, 65),
            np.percentile(x, 70),
            np.percentile(x, 75),
            np.percentile(x, 80),
            np.percentile(x, 85),
            np.percentile(x, 90),
            np.percentile(x, 95),
            np.percentile(x, 99),
        ]
        res = [
            "> p90 (%s)" % (lspercentile[8])
            if z > lspercentile[8]
            else "<= p10 (%s)" % (lspercentile[0])
            if z <= lspercentile[0]
            else "<= p20 (%s)" % (lspercentile[1])
            if z <= lspercentile[1]
            else "<= p30 (%s)" % (lspercentile[2])
            if z <= lspercentile[2]
            else "<= p40 (%s)" % (lspercentile[3])
            if z <= lspercentile[3]
            else "<= p50 (%s)" % (lspercentile[4])
            if z <= lspercentile[4]
            else "<= p60 (%s)" % (lspercentile[5])
            if z <= lspercentile[5]
            else "<= p70 (%s)" % (lspercentile[6])
            if z <= lspercentile[6]
            else "<= p80 (%s)" % (lspercentile[7])
            if z <= lspercentile[7]
            else "<= p90 (%s)" % (lspercentile[8])
            if z <= lspercentile[8]
            else "> p90 (%s)" % (lspercentile[8])
            for z in x
        ]
    elif granularity == "Medium":
        lspercentile = [
            np.percentile(x, 10),
            np.percentile(x, 20),
            np.percentile(x, 30),
            np.percentile(x, 40),
            np.percentile(x, 50),
            np.percentile(x, 60),
            np.percentile(x, 70),
            np.percentile(x, 80),
            np.percentile(x, 90),
        ]
        res = [
            "<= p10 (%s)" % (lspercentile[0])
            if z <= lspercentile[0]
            else "<= p20 (%s)" % (lspercentile[1])
            if z <= lspercentile[1]
            else "<= p30 (%s)" % (lspercentile[2])
            if z <= lspercentile[2]
            else "<= p40 (%s)" % (lspercentile[3])
            if z <= lspercentile[3]
            else "<= p50 (%s)" % (lspercentile[4])
            if z <= lspercentile[4]
            else "<= p60 (%s)" % (lspercentile[5])
            if z <= lspercentile[5]
            else "<= p70 (%s)" % (lspercentile[6])
            if z <= lspercentile[6]
            else "<= p80 (%s)" % (lspercentile[7])
            if z <= lspercentile[7]
            else "<= p90 (%s)" % (lspercentile[8])
            if z <= lspercentile[8]
            else "> p90 (%s)" % (lspercentile[8])
            for z in x
        ]
    else:
        lspercentile = [
            np.percentile(x, 15),
            np.percentile(x, 50),
            np.percentile(x, 85),
        ]
        res = [
            "1-Very Low"
            if z < lspercentile[0]
            else "2-Low"
            if z < lspercentile[1]
            else "3-High"
            if z < lspercentile[2]
            else "4-Very High"
            for z in x
        ]
    return res


def kpi_transform(dfx, kpi_combo, kpi_combo_new):
    """
    Feature transformation from continuous feature to binned features for a list of features

    Args
    ----

    dfx : DataFrame
        DataFrame containing the features.

    kpi_combo : list of string
        List of feature names to be transformed

    kpi_combo_new : list of string
        List of new feature names to be assigned to the transformed features.

    Returns
    -------
    dfx : DataFrame
        Updated DataFrame containing the new features.
    """
    for j in range(len(kpi_combo)):
        if type(dfx[kpi_combo[j]].values[0]) is str:
            dfx[kpi_combo_new[j]] = dfx[kpi_combo[j]].values
            dfx[kpi_combo_new[j]] = cat_group(dfx=dfx, kpix=kpi_combo_new[j])
        else:
            if len(kpi_combo) > 1:
                dfx[kpi_combo_new[j]] = cat_continuous(
                    dfx[kpi_combo[j]].values, granularity="Low"
                )
            else:
                dfx[kpi_combo_new[j]] = cat_continuous(
                    dfx[kpi_combo[j]].values, granularity="High"
                )
    return dfx
